Particle tracking in video microscopy involves tracking particle movement over time in video microscopy frames. Software is available for semi-automated tracking of particles across video microscopy frames. However, existing software must be manually tuned for video microscopy imaging and experimental conditions, such as background lighting, particle size, noise, and particle diffusivity, associated with each video microscopy data set. Such conditions vary across video microscopy data sets. As a result, human intervention and expertise are required to tune or configure the software for optimal particle tracking in each individual video microscopy dataset. Due to the variations in imaging and experimental conditions across different video microscopy data sets and the need to manually tune tracking software for each individual set of conditions, existing tracking software is sub-optimal for automated tracking across different video microscopy datasets. In addition, because human intervention is required to configure the tracking software for each analysis, tracking results are not reproducible.
Accordingly, in light of these difficulties, there exists a need for improved methods, systems, and computer readable media for automated tracking of particles in diverse video microscopy data sets.